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Power policy quantification based on PMC index model and its application in load forecasting
- Chinese Journal of Intelligent Science and Technology Vol. 3, Issue 2, Pages: 202-210(2021)
- 作者机构：
1. 国家电网有限公司华中分部，湖北 武汉 430077
2. 武汉大学电气与自动化学院，湖北 武汉 430072
- 作者简介：
- 基金信息：
DOI：10.11959/j.issn.2096-6652.202121
CLC： TM714-
Published Online：2021-06，
Published：15 June 2021
- 稿件说明：
移动端阅览
Tianbin LIU, Hang ZHAO, Chen WANG, et al. Power policy quantification based on PMC index model and its application in load forecasting[J]. Chinese journal of intelligent science and technology, 2021, 3(2): 202-210.
Tianbin LIU, Hang ZHAO, Chen WANG, et al. Power policy quantification based on PMC index model and its application in load forecasting[J]. Chinese journal of intelligent science and technology, 2021, 3(2): 202-210. DOI： 10.11959/j.issn.2096-6652.202121.
摘要
政策对电力系统负荷有直接的影响。为了充分挖掘政策因素与负荷之间的关系，提高负荷预测的精度，提出一种基于政策建模一致性（PMC）指数的电力政策量化方法，并将其应用到负荷预测中。首先建立电力领域PMC评价体系，然后通过文本挖掘技术得到电力政策文本的PMC指数，最后构建基于长短期记忆的负荷预测模型，将电力政策量化指标与天气、日期等影响因素一同输入模型中，与不考虑政策因素的模型进行对比。实验表明，考虑政策因素的负荷预测模型获得了较好的结果，加入政策量化数据后，负荷预测模型的平均绝对百分比误差从1.67降低到0.98，平均绝对误差从28.97降低到19.68，这表明PMC模型具有一定的政策量化能力。
Abstract
Policy has a direct impact on power system load.In order to fully explore the relationship between policy factors and load
and improve the accuracy of load forecasting
a quantitative method of power policy based on policy modeling consistency (PMC) index was proposed and it was applied to load forecasting.Firstly
the PMC evaluation system of electric power field was established
and then the PMC index of power policy text was obtained by text mining technology.Finally
the load forecasting model based on long shrot term memory was constructed.The quantitative index of power policy
weather
date and other influencing factors were input into the model
and compared with the model without considering policy factors.The experiment shows that the load forecasting model with policy factors achieves good results.After adding policy quantitative data
the error mean absolute percentage error of load forecasting model is reduced from 1.67 to 0.98
and mean absolute error is reduced from 28.97 to 19.68
which indicates that PMC model has a certain ability of policy quantification.
关键词
Keywords
references
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